Identifying key features for determining the patterns of patients with functional dyspepsia using machine learning.

ABSTRACT:

Background and aims

Pattern identification (PI) provides a basis for understanding disease symptoms and signs. The aims of this study are to extract features for identifying conventional PI types from the questionnaire data of patients with functional dyspepsia (FD) through supervised learning methods, and to compare them with another set of features for novel PI types identified with unsupervised learning.

Methods

In total, 153 patients with FD were invited to complete the Standardized Tool for Pattern Identification of Functional Dyspepsia (STPI-FD) questionnaire. Supervised analysis using support vector machine (SVM) was conducted to extract the most discriminative features. For unsupervised analysis, t-distributed stochastic neighbor embedding (t-SNE) and k-means cluster

SUBMITTER: Moon H 

PROVIDER: S-EPMC12549581 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature